Data Science, Database, AI Startups and Domain Learning's (Video-Image-Text-Data-Database): Data Governance

December 17, 2020

Data Governance - Research paper Reads

Data Governance for Platform Ecosystems: Critical Factors and the State of Practice

Key Notes

Data Challenges - data abuse, privacy violation and proper distribution
Data Governance - (availability, usability, security and privacy)

Data Governance Factors

Data Ownership access - presents who owns and uses the data in platform ecosystems.
Data ownership definition criteria
Monitoring - Invisible supply chain is a longstanding challenge
Conformance - Audit for compliance based on strict processes and rules
Data Use Case - Use the data in platform ecosystems
Data provenance - Data transparently for all participating groups
Contribution Estimation - User contribution against value creation by providing data

AI GOVERNANCE FOR BUSINESSES

Key Notes

AI exhibits forms of intelligent behavior allowing for a large range of cost-efficient, wellperforming applications
AI produces results that are partly outside the control of an organization or at least unexpected. It exhibits non-predictable, “ethics”-unaware, data-induced behavior yielding novel security, safety and fairness issues
To mitigate AI challenges and to raise AI potentials in organisations, governance mechanisms play an important role
Testing of ML models, ensuring fairness, explaining “black boxes”, data valuation
Data-driven lens of AI, based on the observation that most existing AI techniques
Prominent regulations include the European GDPR that touches upon data as well as models. Compliance monitoring, Audit

Data

Data is the representation of facts using text, numbers, images, sound or video
An essential characteristic of data is in addition also the primary source of data: Is it personal or non-personal?
GDPR 2018 grants the right to explanation to individuals for automated decisions based on their data
Governance model based on fairness, transparency, trustworthiness, accountability.

Model Explainability

Transparent models are intrinsically human understandable, whereas complex black-box models such as deep learning require external methods that provide explanations that might or might not suffice to understand the model

Data Valuation - valuation of data gains in relevance, if the acquisition of data comes with costs, e.g. data has to be labeled by humans as part of the construction of a dataset for an AI system or data requires costly processing, such as manual cleansing to raise data quality

Data quality denotes the ability of data to meet its usage requirements in a given context
An important data quality aspect with respect to fairness of ML systems is bias
Data is biased if it is not representative of the population or phenomenon of study.
Concept drift implies that the data used to train ML model does not capture the relationship that the model should capture
Robustness defines to what extent a ML model can function correctly in the presence of invalid inputs
Protected characteristics such as gender, religion, familial status, age and race must not be used
ML should allow to track provenance/lineage, ensure reproducability, enable audits and compliance checks of models, foster reusability, handle scale and heterogeneity, allow for flexible metadata usage

Unionized Data Governance in Virtual Power Plants

Collective bargaining The asset-owners should be able to bargain collectively about the conditions and purposes of the data flows. This includes which supplementary data flows to include and how to utilize them

Representation The asset-owners should be represented in a central organizational governing body, which is in charge of defining and overseeing the data principles.

Accountability Transparency measures should be put in place to ensure the asset-owners ability to audit the data usage performed by the aggregator, in order to detect misuse and assign accountability

Social and Governance Implications of Improved Data Efficiency

Data Readiness Report